Bottom Line:
Cluster A was the least stable (21 % stability) and cluster B was the most stable cluster (71 % stability).Cluster stability was not influenced by changes in the dosage of inhaled corticosteroids.This finding indicates that the majority of patients within stable clusters can be phenotyped with reasonable accuracy after a single measurement of lung function and sputum eosinophilia, while patients in unstable clusters will require more frequent evaluation of these variables to be properly characterized.

Affiliation: Department of Mathematics, The College at Brockport, State University of New York, Brockport, NY, USA.

ABSTRACT

Background: Although the heterogeneous nature of asthma has prompted asthma phenotyping with physiological or biomarker data, these studies have been mostly cross-sectional. Longitudinal studies that assess the stability of phenotypes based on a combination of physiological, clinical and biomarker data are currently lacking. Our objective was to assess the longitudinal stability of clusters derived from repeated measures of airway and physiological data over a 1-year period in moderate and severe asthmatics.

Methods: A total of 125 subjects, 48 with moderate asthma (MA) and 77 with severe asthma (SA) were evaluated every 3 months and monthly, respectively, over a 1-year period. At each 3-month time point, subjects were grouped into 4 asthma clusters (A, B, C, D) based on a combination of clinical (duration of asthma), physiological (FEV1 and BMI) and biomarker (sputum eosinophil count) variables, using k-means clustering.

Results: Majority of subjects in clusters A and C had severe asthma (93 % of subjects in cluster A and 79.5 % of subjects in cluster C at baseline). Overall, a total of 59 subjects (47 %) had stable cluster membership, remaining in clusters with the same subjects at each evaluation time. Cluster A was the least stable (21 % stability) and cluster B was the most stable cluster (71 % stability). Cluster stability was not influenced by changes in the dosage of inhaled corticosteroids.

Conclusion: Asthma phenotyping based on clinical, physiologic and biomarker data identified clusters with significant differences in longitudinal stability over a 1-year period. This finding indicates that the majority of patients within stable clusters can be phenotyped with reasonable accuracy after a single measurement of lung function and sputum eosinophilia, while patients in unstable clusters will require more frequent evaluation of these variables to be properly characterized.

Fig1: Cluster membership over time1, by baseline cluster membership. 1Cluster membership at baseline is indicated by the bar colours. The graph depicts how subjects are clustered together over time

Mentions:
Overall, the prevalence range of the clusters across all five-time points was: Cluster A [12–20 %], Cluster B [13–30 %], Cluster C [20–31 %] and Cluster D [40–43 %]. To study temporal stability of the clusters, we estimated the subject flux from one cluster to another along with similarity indices between one cluster and another at baseline, 3, 6, 9 and 12 months (Table 4). Cluster A was the least stable of the 4 clusters; 3 out of 15 subjects (20 %) allocated at baseline to cluster A remained in the same cluster over time. Cluster B was the most stable: 12 out of 17 (71 %) allocated at baseline to cluster B remained together at each time point. Cluster C and D were intermediate: with 20 out of 39 (51 %) clustered at baseline in cluster C staying in the same cluster at each time point, and 31 of 54 (57 %) subjects in cluster D remaining in the same cluster at each time point. Figure 1 displays cluster membership at baseline and how subjects clustered at baseline were clustered at 3, 6, 9 and 12 months.Table 4

Fig1: Cluster membership over time1, by baseline cluster membership. 1Cluster membership at baseline is indicated by the bar colours. The graph depicts how subjects are clustered together over time

Mentions:
Overall, the prevalence range of the clusters across all five-time points was: Cluster A [12–20 %], Cluster B [13–30 %], Cluster C [20–31 %] and Cluster D [40–43 %]. To study temporal stability of the clusters, we estimated the subject flux from one cluster to another along with similarity indices between one cluster and another at baseline, 3, 6, 9 and 12 months (Table 4). Cluster A was the least stable of the 4 clusters; 3 out of 15 subjects (20 %) allocated at baseline to cluster A remained in the same cluster over time. Cluster B was the most stable: 12 out of 17 (71 %) allocated at baseline to cluster B remained together at each time point. Cluster C and D were intermediate: with 20 out of 39 (51 %) clustered at baseline in cluster C staying in the same cluster at each time point, and 31 of 54 (57 %) subjects in cluster D remaining in the same cluster at each time point. Figure 1 displays cluster membership at baseline and how subjects clustered at baseline were clustered at 3, 6, 9 and 12 months.Table 4

Bottom Line:
Cluster A was the least stable (21 % stability) and cluster B was the most stable cluster (71 % stability).Cluster stability was not influenced by changes in the dosage of inhaled corticosteroids.This finding indicates that the majority of patients within stable clusters can be phenotyped with reasonable accuracy after a single measurement of lung function and sputum eosinophilia, while patients in unstable clusters will require more frequent evaluation of these variables to be properly characterized.

Affiliation:
Department of Mathematics, The College at Brockport, State University of New York, Brockport, NY, USA.

ABSTRACT

Background: Although the heterogeneous nature of asthma has prompted asthma phenotyping with physiological or biomarker data, these studies have been mostly cross-sectional. Longitudinal studies that assess the stability of phenotypes based on a combination of physiological, clinical and biomarker data are currently lacking. Our objective was to assess the longitudinal stability of clusters derived from repeated measures of airway and physiological data over a 1-year period in moderate and severe asthmatics.

Methods: A total of 125 subjects, 48 with moderate asthma (MA) and 77 with severe asthma (SA) were evaluated every 3 months and monthly, respectively, over a 1-year period. At each 3-month time point, subjects were grouped into 4 asthma clusters (A, B, C, D) based on a combination of clinical (duration of asthma), physiological (FEV1 and BMI) and biomarker (sputum eosinophil count) variables, using k-means clustering.

Results: Majority of subjects in clusters A and C had severe asthma (93 % of subjects in cluster A and 79.5 % of subjects in cluster C at baseline). Overall, a total of 59 subjects (47 %) had stable cluster membership, remaining in clusters with the same subjects at each evaluation time. Cluster A was the least stable (21 % stability) and cluster B was the most stable cluster (71 % stability). Cluster stability was not influenced by changes in the dosage of inhaled corticosteroids.

Conclusion: Asthma phenotyping based on clinical, physiologic and biomarker data identified clusters with significant differences in longitudinal stability over a 1-year period. This finding indicates that the majority of patients within stable clusters can be phenotyped with reasonable accuracy after a single measurement of lung function and sputum eosinophilia, while patients in unstable clusters will require more frequent evaluation of these variables to be properly characterized.